Сравнение методов

	Интегрированное с анализом чувствительности планирование экспериментов	Латинское гиперкубическое проектирование
Область	Планирование эксперимента	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1990s–2000s (formal integration emerged in simulation and engineering optimization literature)	1979
Автор метода	Integrated approach drawing on Saltelli et al. (sensitivity analysis) and Montgomery (DoE); no single originator	—
Тип	Hybrid experimental-analytical framework	Stratified space-filling sampling design
Основополагающий источник	Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Wiley. ISBN: 9780470870938	McKay, M.D., Beckman, R.J. & Conover, W.J. (1979). A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics, 21(2), 239-245.
Другие названия	SA-DoE, SA-integrated DoE, DoE with sensitivity screening, factor screening with sensitivity analysis	LHS, Latin Hiperküp Örnekleme (LHS) ve Duyarlılık Analizi, stratified sampling design, space-filling design
Связанные	3	4
Сводка	Sensitivity Analysis-Integrated Design of Experiments (SA-DoE) combines systematic experimental planning with formal sensitivity analysis to identify which input factors most strongly influence a response, then efficiently characterises those factors' effects. By embedding sensitivity screening into the DoE workflow, experimenters avoid wasting trials on inert variables and focus resources on the factors that truly drive system behaviour — making it especially valuable in simulation studies, product engineering, and complex process optimisation.	Latin Hypercube Sampling (LHS) is a stratified space-filling design for computer experiments, introduced by McKay, Beckman, and Conover in 1979. It divides each input variable's range into equally probable strata and draws exactly one sample per stratum, ensuring that the full input space is covered with far fewer model evaluations than standard Monte Carlo simulation requires. It is routinely paired with global sensitivity analysis — particularly Sobol indices — to quantify how much each input drives output variability.